add ddqn

codes/ddpg/ddpg.py

@@ -5,7 +5,7 @@
 @Email: johnjim0816@gmail.com
 @Date: 2020-06-09 20:25:52
 @LastEditor: John
-@LastEditTime: 2020-06-14 11:43:17
+LastEditTime: 2020-09-02 01:19:13
 @Discription: 
 @Environment: python 3.7.7
 '''
@@ -35,39 +35,41 @@ class DDPG:
         self.critic_optimizer = optim.Adam(
             self.critic.parameters(),  lr=critic_lr)
         self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=actor_lr)
-        self.critic_criterion = nn.MSELoss()
         self.memory = ReplayBuffer(memory_capacity)
         self.batch_size = batch_size
         self.soft_tau = soft_tau
         self.gamma = gamma
 
     def select_action(self, state):
-        return self.actor.select_action(state)
+        state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
+        action = self.actor(state)
+        # torch.detach()用于切断反向传播
+        return action.detach().cpu().numpy()[0, 0]
 
     def update(self):
         if len(self.memory) < self.batch_size:
             return
         state, action, reward, next_state, done = self.memory.sample(
-            self.batch_size) 
+            self.batch_size)
         # 将所有变量转为张量
         state = torch.FloatTensor(state).to(self.device)
         next_state = torch.FloatTensor(next_state).to(self.device)
         action = torch.FloatTensor(action).to(self.device)
         reward = torch.FloatTensor(reward).unsqueeze(1).to(self.device)
-        
         done = torch.FloatTensor(np.float32(done)).unsqueeze(1).to(self.device)
+        # 注意critic将(s_t,a)作为输入
         policy_loss = self.critic(state, self.actor(state))
+        
         policy_loss = -policy_loss.mean()
 
         next_action = self.target_actor(next_state)
         target_value = self.target_critic(next_state, next_action.detach())
         expected_value = reward + (1.0 - done) * self.gamma * target_value
         expected_value = torch.clamp(expected_value, -np.inf, np.inf)
-        
+
         value = self.critic(state, action)
-
-        value_loss = self.critic_criterion(value, expected_value.detach())
-
+        value_loss = nn.MSELoss()(value, expected_value.detach())
+        
         self.actor_optimizer.zero_grad()
         policy_loss.backward()
         self.actor_optimizer.step()
@@ -85,3 +87,8 @@ class DDPG:
                 target_param.data * (1.0 - self.soft_tau) +
                 param.data * self.soft_tau
             )
+    def save_model(self,path):
+        torch.save(self.target_actor.state_dict(), path)
+
+    def load_model(self,path):
+        self.actor.load_state_dict(torch.load(path))